Open Access

RETRACTION

Wei-Lung Mao^1,*, Yu-Ying Chiu¹, Chao-Ting Chu², Bing-Hong Lin¹, Jian-Jie Hung³
Intelligent Automation & Soft Computing, Vol.41, pp. 47-47, 2026, DOI:10.32604/iasc.2026.089959 - 19 August 2026
Abstract This article has no abstract. More >

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ARTICLE

Sugeng Rifqi Mubaroq^1,*, Rolly Maulana Awangga², Tegar Ditya Pragama¹, Sidiq Fathummubin³, Ali Yusuf Abdulhaq¹
Intelligent Automation & Soft Computing, Vol.41, pp. 27-46, 2026, DOI:10.32604/iasc.2026.088039 - 11 August 2026
Abstract Decoding cognitive workload from electroencephalography (EEG) underpins passive brain–computer interfaces and adaptive learning technology, yet practical decoders share two weaknesses: they rely on a single family of features, and their accuracy collapses on recordings from an unfamiliar device, montage, or task. We address both. We first build a multi-specialist stacking decoder that fuses complementary spectral, Riemannian, and spatial views through a meta-learner. Evaluated across two public corpora under the leave-one-subject-out MOABB benchmarking protocol, it outperforms the best single specialist on the binary workload contrasts, and the strongest of these effects survives family-wide false-discovery-rate correction. The… More >

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107

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CORRECTION

S. Saranya^*, G. Usha
Intelligent Automation & Soft Computing, Vol.41, pp. 25-25, 2026, DOI:10.32604/iasc.2026.085938 - 04 June 2026
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ARTICLE

Thanh Tuan Nguyen¹, Cuong Nguyen Dinh Hoa^2,3,*
Intelligent Automation & Soft Computing, Vol.41, pp. 1-24, 2026, DOI:10.32604/iasc.2026.078344 - 12 May 2026
Abstract Accurate crude oil price forecasting is critical for global economic stability but remains an exceptionally challenging task due to the data’s complex, non-linear, and non-stationary nature. Deep learning models like LSTMs are widely favored. However, the dominant research trend currently focuses on increasingly complex hybrid and ensemble architectures. These models often suffer from high computational overhead, intricate tuning processes, and potential overfitting, raising critical questions about their necessity. In this paper, we challenged the assumption that complexity is required for high performance by proposing and evaluating a streamlined 1D-CNN model. We conducted a comprehensive evaluation… More >

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791